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Random forest and extremely random forest models forecast roughened monolithic butadiene styrene rubber surface area from abrasive paper grain diameter. Results show a very strong negative correlation between roughness and grain diameter, with Pearson r = -0.83 and Spearman ρ = -0.91. Shapiro–Wilk statistics confirm normal distribution of profile lengths regardless of grain diameter. Optimized forest parameters achieved strong agreement (R2 = 0.95) and relative approximation error not exceeding 0.079%.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-methods-for-the-prediction-of-abraded-butadiene-styrene-rubber-surface-roughness-research-and-prediction/291046/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-methods-for-the-prediction-of-abraded-butadiene-styrene-rubber-surface-roughness-research-and-prediction/291046.png","ImageObject",300,407,{"name":92,"@type":93},"Genevieve","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-19","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the study aiming to predict?","Question",{"text":113,"@type":114},"The study aims to predict the roughened surface area of abraded butadiene-styrene rubber surfaces using machine learning.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which machine learning methods are used?",{"text":118,"@type":114},"Random forest and extremely random forest are used to model the relationship between abrasive grain diameter and predicted surface area.",{"name":120,"@type":111,"acceptedAnswer":121},"How strong is the relationship between surface roughness and abrasive grain diameter?",{"text":122,"@type":114},"Surface roughness shows a very strong negative correlation with abrasive grain diameter, with Pearson r = -0.83 and Spearman ρ = -0.91.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291046,1789643306,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},1374391974585,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","ISSN 1392–1320 MATERIALS SCIENCE (MEDŽIAGOTYRA). Vol. 29, No. 1. 2023  \nMachine Learning Methods for the Prediction of Abraded Butadiene-Styrene Rubber Surface Roughness  \nLoreta MAČĖNAITĖ 1 ∗, Dovydas LUGAS 1, Eglė FATARAITĖ-URBONIENĖ 2, 3  \n1 Kaunas University of Technology, Faculty of Mathematics and Natural Sciences, Studentų st. 50, LT- 51368 Kaunas, Lithuania  \n2 Kaunas University of Technology, Faculty of Mechanical Engineering and Design, Studentų st. 56, LT-51424 Kaunas, Lithuania  \n3 Applied Research Institute for Prospective Technologies, Vismaliukų st. 34, LT-10243 Vilnius, Lithuania  \n [http://dx.doi.org/10.5755/j02.ms.31288](http://dx.doi.org/10.5755/j02.ms.31288)  \nReceived 28 April 2022; accepted 03 May 2022  \nThe aim of this investigation was to evaluate the ability to predict the roughened surface area of soft polymeric materials using the machine learning method. Random forest and extremely random forest machine learning methods have been applied to predict roughened monolithic butadiene styrene rubber surface area based on the grain diameter of the abrasive paper. It was found that there is a very strong negative correlation between surface roughness and abrasive grain diameter (the Pearson correlation coefficient is equal to -0.83 and the Spearman correlation coefficient is equal to -0.91) . The statistics of Shapiro-Wilk criteria confirmed that independently of the diameter of abrasive grain, the lengths of rubber surface profiles are normally distributed. It was determined that random forest and extremely random forest algorithms are suitable tools for predicting rubber surface area in dependence on abrasive grain diameter. The forest structure in which results obtained best coincidence with the observed (R2 = 0.95) : for the random forest model the number of trees is equal to 20, the leaf size is equal to 4, at the subset of the feature (abrasive paper grain diameter) equal to 1. In the case of an extremely random forest, the number of trees is equal to 350, the size of the subset of the features is equal to 1, and the leaf size is 2. The experimental and generated results agree well. The relative approximation error does not exceed 0.079 % .  \nKeywords: surface roughness, prediction, machine learning, random forest, extremely random forest.  \n1. INTRODUCTION  \nAdhesive joints for soft polymeric materials are widely used in many applications and are subject to extensive research to evaluate their durability [1]. The results of many researchers indicate that not only the chemical nature and compatibility of materials results in the strength of adhesive joints but also a very important parameter is the size of the contact area, which, as a rule, can be increased by mechanical surface roughening [2–6] . This kind of treatment is also used to remove surface impurities, but the main effect of surface abrasion is an increase in contact surface area. It is shown, that by variation of surface roughness parameters the strength of adhesive joints for rubber substrate can be increased from 25 % to 90 % compared to those ofunroughened samples [7] .  \nThe bond contact area can only be found during the experiment. The method of contact or noncontact profilometry is usually used to obtain surface roughness characteristics. The surface is examined by a needle or laser beam perpendicular to the surface to be examined and transmits its image in the form of electrical signals to a digital one.  \nSince the experiment is usually a time-consuming process, it is important to create a model that would allow predicting the real surface area of the substrate depending on the surface roughness characteristics without performing a large experiment.  \n∗ Corresponding author. Tel.: +370-611-11898.  \nE-mail address: [loreta.macenaite@ktu.lt](loreta.macenaite@ktu.lt) (L. Macenaite)  \nOne way to determine the surface roughness parameters is based on the theory of random functions. In this case, the rough surface profile is assumed to b","cbCaijIlYRmSnfeR","https://ap.wps.com/l/cbCaijIlYRmSnfeR","pdf",403664,"English","# 1. Introduction\n## Adhesive joints and contact area relevance\n## Profilometry for surface roughness measurement\n## Need for predictive modeling\n## Random function and contact mechanics background","[{\"question\":\"What is the study aiming to predict?\",\"answer\":\"The study aims to predict the roughened surface area of abraded butadiene-styrene rubber surfaces using machine learning.\"},{\"question\":\"Which machine learning methods are used?\",\"answer\":\"Random forest and extremely random forest are used to model the relationship between abrasive grain diameter and predicted surface area.\"},{\"question\":\"How strong is the relationship between surface roughness and abrasive grain diameter?\",\"answer\":\"Surface roughness shows a very strong negative correlation with abrasive grain diameter, with Pearson r = -0.83 and Spearman ρ = -0.91.\"}]","Machine Learning Methods for the Prediction of Abraded Butadiene-Styrene Rubber Surface Roughness - research and prediction | PDF",15]